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--- news:news [2021/04/25 21:58] – Wolfgang Viechtbauer
+++ news:news [2022/01/02 13:51] – Wolfgang Viechtbauer
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 ~~NOTOC~~
-==== April 21st, 2021: Better Degrees of Freedom Calculation ====
+==== 2022-01-02: More Forest Plot Examples ====
-In random/mixed-effects models as can be fitted with the [[https://wviechtb.github.io/metafor/reference/rma.html|rma()]] function, tests and confidence intervals for the model coefficients are by default constructed based on a standard normal distribution. In general, it is better to use the Knapp-Hartung method for this purpose, which does two things: (1) the standard errors of the model coefficients are estimated in a slightly different way and (2) a t-distribution is used with $k-p$ degrees of freedom (where $k$ is the total number of estimates and $p$ the number of coefficients in the model). When conducting a simultaneous (or 'omnibus') test of multiple coefficients, then an F-distribution with $m$ and $k-p$ degrees of freedom is used (for the 'numerator' and 'denominator' degrees of freedom, respectively), with $m$ denoting the number of coefficients tested. To use this method, set argument ''test="knha"''.
+Happy New Year! Hope this one will be at least marginally less crazy than the previous ones ...
-The Knapp-Hartung method cannot be directly generalized to more complex models as can be fitted with the [[https://wviechtb.github.io/metafor/reference/rma.mv.html|rma.mv()]] function, although we can still use t- and F-distributions for conducting tests of one or multiple model coefficients in the context of such models. This is possible by setting ''test="t"''. However, this then raises the question how the (denominator) degrees of freedom for such tests should be calculated. By default, the degrees of freedom are calculated as described above. However, this method does not reflect the complexities of models that are typically fitted with the ''rma.mv()'' function. For example, in multilevel models (with multiple estimates nested within studies), a predictor (or 'moderator') may be measured at the study level (i.e., it is constant for all estimates belonging to the same study) or at the level of the individual estimates (i.e., it might vary within studies). By setting argument ''dfs="contain"'', a method is used for calculating the degrees of freedom that tends to provide tests with better control of the Type I error rate and confidence intervals with closer to nominal coverage rates. See the documentation of the function for further details.
+I was recently asked whether I would add the feature to show multiple confidence intervals for each of the studies in a forest plot (e.g., by using lines with varying thickness) to the metafor package. Turns out that one can already do this without too much difficulty using the existing tools, simply by superimposing two forest plots on top of each other. This is illustrated [[plots:forest_plot_with_multiple_cis|here]].
-==== April 3rd, 2021: Scatter Plots / Bubble Plots for Meta-Regression Models ====
+I also wanted to see to what extent one can reproduce forest plots created by different software or using the aesthetics of certain journals. I started with the recreation of a forest plot that was obtained using RevMan, the software provided by the Cochrane Collaboration for conducting and authoring Cochrane reviews. You can find the figure and corresponding code for this [[plots:forest_plot_revman|here]]. Then I recreated a forest plot that was obtained from an article in the British Medical Journal. The resulting figure and code can be found [[plots:forest_plot_bmj|here]].
-I finally got around to adding a function to the package for drawing scatter plots (also known as bubble plots) for meta-regression models. See the documentation of the [[https://wviechtb.github.io/metafor/reference/regplot.html|regplot()]] function for further details. An example illustrating such a plot is provided [[plots:meta_analytic_scatterplot|here]].
+Although it takes a bit of effort to recreate these figures (especially if one wants to make them look almost identical to the originals), it shows that one can essentially recreate any forest plot using the various ''forest()'' functions from metafor and then some additional functions like ''text()'', ''points()'', and so on, which give you full control over how things are drawn and the information included in the figure.